FAIR Data Pipeline: provenance-driven data management for traceable scientific workflows

TL;DR

This paper presents a FAIR data pipeline that enhances transparency and traceability in scientific workflows, particularly during epidemiological crises like COVID-19, by enabling provenance tracking from data to outputs.

Contribution

The paper introduces a provenance-driven data management tool that integrates FAIR principles into epidemiological workflows, improving transparency and trust in scientific evidence.

Findings

Enables annotation of data during analysis

Allows tracing of outputs back to original data sources

Supports transparent policy decision-making

Abstract

Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging. Data management is further complicated by data being imprecisely identified when used. Public trust in policy decisions resulting from such analyses is easily damaged and is often low, with cynicism arising where claims of "following the science" are made without accompanying evidence. Tracing the provenance of such decisions back through open software to primary data would clarify this evidence, enhancing the transparency of the decision-making process. Here, we demonstrate a Findable, Accessible, Interoperable and Reusable (FAIR) data pipeline developed during the COVID-19 pandemic that allows easy annotation of data as they are consumed by analyses, while tracing the provenance of scientific outputs back through the analytical source code to data sources. Such a tool provides a mechanism for the public, and fellow scientists, to better assess the trust that should be placed in scientific evidence, while allowing scientists to support policy-makers in openly justifying their decisions. We believe that tools such as this should be promoted for use across all areas of policy-facing research.